# SPDS
library(tidyverse)
library(sf)
library(units)
library(dplyr)
library(readr)
library(knitr)

# Data
library(USAboundaries)
library(rnaturalearth)

# Visualization
library(ggplot2)
library(gghighlight)
library(ggrepel)
library(knitr)

Question 1

1.1 Define a Projection

eqdc = '+proj=eqdc +lat_0=40 +lon_0=-96 +lat_1=20 +lat_2=60 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs'

1.2 USA State Boundaries

USAboundaries::us_states(resolution = "low") %>%
  st_as_sf(coords = c("lng", "lat"), crs = 4326) %>%
  filter(!state_name %in% c("Puerto Rico",
                            "Alaska",
                            "Hawaii")) ->
  conus
st_transform(conus, eqdc) -> conus

1.3 Mexico, United States, and Canada

rnaturalearthdata::countries110 -> countries110
st_as_sf(countries110, crs = 4326) -> worldboundaries
st_transform(worldboundaries, eqdc) -> worldboundaries

1.4 City Locations

readr::read_csv("~/github/geog-13-labs/data/US Cities.csv") %>%
  st_as_sf(coords = c("lng", "lat"), crs = 4326) %>%
  filter(!state_name %in% c("Hawaii", "Alaska", "Puerto Rico")) %>%
         st_transform(., eqdc) ->
  uscities

Question 2

2.1 Distance to USA Border

st_union(conus) %>%
  st_cast("MULTILINESTRING") ->
  USboundaries
uscities %>%
  select(city, state_name) %>%
  mutate(dist = st_distance(uscities, USboundaries),
  dist = units::set_units(dist, "km"),
  dist = units::drop_units(dist)) ->
  border_dist
border_dist %>%
  slice_max(dist, n = 5) %>%
  st_drop_geometry() ->
  top5_to_border
knitr::kable(top5_to_border,
             caption = "Distance of US Cities to US Borders",
             col.names = c("City", "State", "Distance to Border"))
Distance of US Cities to US Borders
City State Distance to Border
Dresden Kansas 1012.317
Herndon Kansas 1007.750
Hill City Kansas 1005.147
Atwood Kansas 1004.734
Jennings Kansas 1003.646

2.2 Distance to States

st_combine(conus) %>%
  st_cast("MULTILINESTRING") ->
  USboundaries2
uscities %>%
  select(city, state_name) %>%
  mutate(dist2 = st_distance(uscities, USboundaries2),
         dist2 = units::set_units(dist2, "km"),
         dist2 = units::drop_units(dist2)) ->
  border_dist2
border_dist2 %>%
  slice_max(dist2, n = 5) %>%
  st_drop_geometry() ->
  top5_to_stateborder
knitr::kable(top5_to_stateborder,
             caption = "Distance of US Cities to US State Borders",
             col.names = c("City", "State", "Distance to State Border"))
Distance of US Cities to US State Borders
City State Distance to State Border
Lampasas Texas 308.9216
Bertram Texas 302.8190
Kempner Texas 302.5912
Harker Heights Texas 298.8125
Florence Texas 298.6804

2.3 Distance to Mexico

worldboundaries %>%
  st_cast("MULTILINESTRING") %>%
  filter(admin == "Mexico") ->
  mexico_border
uscities %>%
  select(city, state_name) %>%
  mutate(dist3 = st_distance(uscities, mexico_border),
         dist3 = units::set_units(dist3, "km"),
         dist3 = units::drop_units(dist3)) ->
  border_dist3
border_dist3 %>%
  slice_max(dist3, n = 5) %>%
  st_drop_geometry() ->
  top5_to_mexico
knitr::kable(top5_to_mexico,
             caption = "Distance of US Cities to Mexican Border",
             col.names = c("City", "State", "Distance to Mexican Border"))
Distance of US Cities to Mexican Border
City State Distance to Mexican Border
Caribou Maine 3250.334
Presque Isle Maine 3234.570
Calais Maine 3134.348
Eastport Maine 3125.624
Old Town Maine 3048.366

2.4 Distance to Canada

worldboundaries %>%
  st_cast("MULTILINESTRING") %>%
  filter(admin == "Canada") ->
  canada_border
uscities %>%
  select(city, state_name) %>%
  mutate(dist4 = st_distance(uscities, canada_border),
         dist4 = units::set_units(dist4, "km"),
         dist4 = units::drop_units(dist4)) ->
  border_dist4
border_dist4 %>%
  slice_max(dist4, n = 5) %>%
  st_drop_geometry() ->
  top5_to_canada
knitr::kable(top5_to_canada,
             caption = "Distance of US Cities to Canadian Border",
             col.names = c("City", "State", "Distance to Canadian Border"))
Distance of US Cities to Canadian Border
City State Distance to Canadian Border
Guadalupe Guerra Texas 2206.455
Sandoval Texas 2205.641
Fronton Texas 2204.784
Fronton Ranchettes Texas 2202.118
Evergreen Texas 2202.020

Question 3

3.1 Data

uscities %>%
  slice_max(population, n = 10) ->
  top10_pop
ggplot() +
  geom_sf(data = conus) +
  geom_sf(data = top10_pop$geometry, add = TRUE, size= 2, color = "red") +
  ggrepel::geom_label_repel(
    data = top10_pop,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 3) +
  labs(title = "10 Largest US Cities by Population") +
  ggthemes::theme_map()

3.2 City Distance from Border

border_dist %>%
  slice_max(dist, n = 5) ->
  top5_to_bordermap
ggplot() +
  geom_sf(data = USboundaries) +
  geom_sf(data = border_dist, aes(col = dist), size = .1) +
  geom_sf(data = top5_to_bordermap, col = "red", size = 1) +
  scale_color_gradient(low = "grey", high = "blue") +
  ggrepel::geom_label_repel(
    data = top5_to_bordermap,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 3) +
  labs(title = "US Cities' Distance from National Border") +
  ggthemes::theme_map()

3.3 City Distance from Nearest State

border_dist2 %>%
  slice_max(dist2, n = 5) ->
  top5_to_statebordermap
ggplot() +
  geom_sf(data = USboundaries2) +
  geom_sf(data = border_dist2, aes(col = dist2), size = .1) +
  geom_sf(data = top5_to_statebordermap, col = "red", size = 1) +
  scale_color_gradient(low = "grey", high = "blue") +
  ggrepel::geom_label_repel(
    data = top5_to_statebordermap,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 3) + 
  labs(title = "US Cities' Distance from State Borders") +
  ggthemes::theme_map()

3.4 Equidistance Boundary from Mexico and Canada

uscities %>%
  select(city, state_name, population) %>%
  mutate(dist3 = st_distance(uscities, mexico_border),
         dist3 = units::set_units(dist3, "km"),
         dist3 = units::drop_units(dist3)) %>%
  mutate(dist4 = st_distance(uscities, canada_border),
         dist4 = units::set_units(dist4, "km"),
         dist4 = units::drop_units(dist4)) %>%
  mutate(diffdist = abs(dist3 - dist4)) ->
  equaldistance
equaldistance %>% 
  filter(diffdist <= 100) %>%
  slice_max(population, n = 5) ->
  ED_pop
ggplot() +
  geom_sf(data = USboundaries2) +
  geom_sf(data = equaldistance, aes(col = diffdist), size = .1) +
  scale_color_gradient(low = "grey", high = "blue") +
  gghighlight::gghighlight(diffdist <= 100) +
  ggrepel::geom_label_repel(
    data = ED_pop,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 3) +
  labs(title = "Cities that are equal distance from the Canadian and Mexican border ± 100 km") +
  ggthemes::theme_map()

Question 4

4.1 Quantifing Border Zone

uscities %>%
  select(city, state_name, population) %>%
  mutate(dist = st_distance(uscities, USboundaries),
         dist = units::set_units(dist, "km"),
         dist = units::drop_units(dist)) %>%
  filter(dist <= 160) %>%
  st_drop_geometry() ->
  bordercities
bordercities %>%
  summarize(population) %>%
  sum()
## [1] 259935815
data.frame(number_cities = "12283",
           pop = "259935815") -> table
knitr::kable(table,
             caption = "Cities Near National Border",
             col.names = c("Number of Cities", "Population"))
Cities Near National Border
Number of Cities Population
12283 259935815

4.2 Mapping Border Zone

ggplot() + 
  geom_sf(data = conus, aes(fill = aland), lty = 3, alpha = .5) + 
  scale_fill_gradient(low = 'blue', high = "red") +
  gghighlight(aland > 5e10) +
  geom_sf(data = conus, size = 1, fill = "NA") +
  geom_sf(data = top10_pop, size= 2, color = "red") +
  ggrepel::geom_label_repel(
    data = top10_pop,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 3) +
  ggthemes::theme_map()